• Geomechanics and Engineering
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• v.34 no.6
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• pp.683-696
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• 2023

The destruction and fracture of rock masses are crucial components in engineering and there is an increasing demand for the study of the influence of rock mass heterogeneity on the safety of engineering projects. The numerical manifold method (NMM) has a unified solution format for continuous and discontinuous problems. In most NMM studies, material homogeneity has been assumed and despite this simplification, fracture mechanics remain complex and simulations are inefficient because of the complicated topology updating operations that are needed after crack propagation. These operations become computationally expensive especially in the cases of heterogeneous materials. In this study, a heterogeneous model algorithm based on stochastic theory was developed and introduced into the NMM. A new fracture algorithm was developed to simulate the rupture zone. The algorithm was validated for the examples of the four-point shear beam and semi-circular bend. Results show that the algorithm can efficiently simulate the rupture zone of heterogeneous rock masses. Heterogeneity has a powerful effect on the macroscopic failure characteristics and uniaxial compressive strength of rock masses. The peak strength of homogeneous material (with heterogeneity or standard deviation of 0) is 2.4 times that of heterogeneous material (with heterogeneity of 11.0). Moreover, the local distribution of parameter values can affect the configuration of rupture zones in rock masses. The local distribution also influences the peak value on the stress-strain curve and the residual strength. The post-peak stress-strain curve envelope from 60 random calculations can be used as an estimate of the strength of engineering rock masses.

• Journal of Korean Neurosurgical Society
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• v.66 no.1
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• pp.53-62
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• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

• Smart Media Journal
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• v.12 no.1
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• pp.17-23
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• 2023

Design-For-Security (DFS) methodology is to protect integrated circuits from physical attacks, and that can be implemented by adding a security circuit to detect abnormal external access. Among the abnormal accesses called invasive attack, microprobing and FIB circuit editing are classified as the most powerful methods because they have direct access. Microprobing deliberately inject defects into the wire of circuit through probes, or reads and changes data. FIB circuit editing is methods of reconnecting or destroying circuits to neutralize security circuits or to access data. Previous DFS methodology have responded to the attacks by detecting arrival time asymmetry between the two signals or by comparing input/output data based on encrypted communication. This study conducted to reduce hardware overhead, and the proposed circuit detects the reflected signal asymmetry generated through probe or FIB circuit editing and detects the attacks through comparison. Since the proposed security circuit reduces the size and test cycle of the circuit compared to previous studies, the cost used for security can be reduced.

• Asia Marketing Journal
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• v.14 no.3
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• pp.7-26
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• 2012

The success story of E-mart fascinated many academics and practitioners alike. Though E-mart began as a nameless discount store in Chang-dong, Seoul in 1993, it has transformed itself into a leading distribution company and one of the most powerful brands in Korea. Surprisingly, it achieved the great success against the two crises it met: the national economic crisis and the invasion of the global giant Walmart. The main objective of this case study is to formally examine how E-mart overcame the two crises. More specifically, this case study highlights the ways with which E-mart turned those difficulties into opportunities for growth. In our examination of the E-mart case, we could clearly see E-mart's competence and spirit that allowed it to turn crises into advantageous opportunities. E-mart attracted the customers who wanted value-oriented consumption by its positioning as the "Lowest price discount store", when consumer sentiment was frozen under the economic crisis. Furthermore, when a large-scale foreign discount store like Walmart entered the Korea market, E-mart built its core competencies as the 'Korean style discount store'. These ingenious positioning and efforts resulted in E-mart taking over their archrival, Walmart, and forced the global Goliath to exit the Korean market. The case of E-mart's effective crisis management teaches many important lessons and a few core lessons that apply to many companies. One such lesson is the importance of positioning which enabled E-mart to turn crises into opportunities. Granted, the strategy of positioning as the 'Korean style discount store', or 'Lowest price discount store' was possible due to overall support with cost reduction, development and management of their own system, an apprentice educate system, etc. based on an excellent selection of location of the store and efficient distribution systems. Still, the positioning strategy of E-mart was truly ground breaking in distancing itself from its competitors. The lessons from E-mart will help those companies currently in a stagnant situation or a crisis to turn their obstacles into great success.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• The Journal of Economics, Marketing and Management
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• v.12 no.2
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• pp.73-81
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• 2024

Purpose: South Korea is a close ally of the US and an important partner of China. Caught between the two most powerful countries, South Korea's strategic directions are critical. This article emphasizes that the deeper core of the US-China trade war is to improve the business environment to attract foreign direct investment (FDI) to boost the economy, rather than engaging in the trade war. Research design, data, and methodology: Considering the complexity of this issue, this article applies a systematic analytical tool, the ABCD (Agility, Benchmarking, Convergence, and Dedication) model, to provide strategic guidance for inducing investments into South Korea in the context of the ongoing US-China trade war. Results: Specifically, South Korea needs to provide a more attractive business environment along the four points: expedite commercial activities through deregulation (Agility); adopt global standards of the flexible labor markets and technological developments (Benchmarking); integrate various industries and connect them to global value chains (Convergence); and create more economy-friendly policies rather than politics-oriented ones such as protectionism (Dedication). Conclusion: This study stands out not just by utilizing the ABCD model but, also by providing more systematic analysis and practical implications, particularly within the context of the escalating US-China competition. Unlike many existing studies that analyze the broader impacts of this geopolitical rivalry, this research delves into specific strategic guidelines for South Korea to attract FDI. The findings also provide implications for multinational corporations (MNCs) in choosing the locations for their overseas operations, particularly in South Korea.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1506-1512
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• 2023

Quantitative analysis of adenosine triphosphate (ATP) has been widely used as a diagnostic tool in the food and medical industries. Particularly, the pathogenesis of a few diseases including inflammatory bowel disease (IBD) is closely related to high ATP concentrations. A bioluminescent D-luciferin/luciferase system, which includes a luciferase (FLuc) from the firefly Photinus pyralis as a key component, is the most commonly used method for the detection and quantification of ATP. Here, instead of isolating FLuc produced in recombinant Escherichia coli, we aimed to develop a whole-cell biocatalyst system that does not require extraction and purification of FLuc. To this end, the gene coding for FLuc was introduced into the genome of probiotic Saccharomyces boulardii using the CRISPR/Cas9-based genome editing system. The linear relationship (r² = 0.9561) between ATP levels and bioluminescence generated from the engineered S. boulardii expressing FLuc was observed in vitro. To explore the feasibility of using the engineered S. boulardii expressing FLuc as a whole-cell biosensor to detect inflammation biomarker (i.e., ATP) in the gut, a colitis mouse model was established using dextran sodium sulfate as a colitogenic compound. Our findings demonstrated that the whole-cell biosensor can detect elevated ATP levels during gut inflammation in mice. Therefore, the simple and powerful method developed herein could be applied for non-invasive IBD diagnosis.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.63-70
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• 2024

Lithium-ion batteries (LIB) are widely used in various sectors, such as transportation (e.g., electric vehicles (EV)) and energy (e.g., energy storage facilities) due to their high energy density, broad operating temperature (-20 ℃ ~ 60 ℃), and high capacities. LIBs are powerful but fragile on external factors, including pressure, physical damage, overheating, and overcharging, that cause thermal runaway causing fires and explosions. During a LIB fire, a large amount of oxygen is generated from the decomposition of ionogenic materials. A water fire extinguisher that helps with cooling and suffocating must be essentially required at the same time. In fact, however, it is difficult to suppress LIB fires in the case of EVs because a LIB is installed with a battery pack housing that interrupts direct extinguishing by water. Thus, this study aims to investigate effective fire extinguishing measurements for LIB fires by using an EV. Relevant documents, including research articles and reports, were reviewed to identify effective ways of LIBs fire extinguishing. A real-scale fire experiment generating thermal runaway was carried out to figure out the combustion characteristics of EVs. This study revealed that the most effective fire extinguishing measurements for LIB fires are applying fire blankets and water tanks. However, there is still a lack of adequate regulation and guidelines for LIB fire extinguishment. Taking this into account, developing functional fire extinguishment measurements and available regulatory instruments is an urgent issue to secure the safety of firefighters and citizens.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.4
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• pp.342-349
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• 2023

Backgrounds/Aims: Liver organoids have emerged as a powerful tool for studying liver biology and disease and for developing new therapies and regenerative medicine approaches. For organoid culture, Matrigel, a type of extracellular matrix, is the most commonly used material. However, Matrigel cannot be used for clinical applications due to the presence of unknown proteins that can cause immune rejection, batch-to-batch variability, and angiogenesis. Methods: To obtain human primary hepatocytes (hPHs), we performed 2 steps collagenase liver perfusion protocol. We treated three small molecules cocktails (A83-01, CHIR99021, and HGF) for reprogramming the hPHs into human chemically derived hepatic progenitors (hCdHs) and used hCdHs to generate liver organoids. Results: In this study, we report the generation of liver organoids in a collagen scaffold using hCdHs. In comparison with adult liver (or primary hepatocyte)-derived organoids with collagen scaffold (hALO_C), hCdH-derived organoids in a collagen scaffold (hCdHO_C) showed a 10-fold increase in organoid generation efficiency with higher expression of liver- or liver progenitor-specific markers. Moreover, we demonstrated that hCdHO_C could differentiate into hepatic organoids (hCdHO_C_DM), indicating the potential of these organoids as a platform for drug screening. Conclusions: Overall, our study highlights the potential of hCdHO_C as a tool for liver research and presents a new approach for generating liver organoids using hCdHs with a collagen scaffold.

• Neonatal Medicine
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• v.18 no.1
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• pp.49-58
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• 2011

Purpose: The purpose of this study was to evaluate the relationship between cord blood adiponectin and insulin-like growth factor (IGF)-I and their effect on fetal growth and insulin resistance in mothers with gestational diabetes mellitus (GDM). Methods: Cord blood adiponectin and IGF-I were compared between mothers with GDM (GDM group, N=53) and controls (non-GDM group, N=101). Neonates were classified into three groups of small for gestational age (SGA, N=26), appropriate for gestational age (AGA, N=97), and large for gestational age (LGA, N=31) by birth weight. The association between cord adiponectin and IGF-I levels was evaluated in relation to maternal and neonatal clinical data. Results: Cord adiponectin was lower in the GDM group than in the non-GDM group (P<0.001). There was no significant difference in cord adiponectin among the SGA, AGA, and LGA groups in the GDM group (P=0.228). The cord adiponectin of AGA in the GDM group was significantly lower than that in the non-GDM group (P<0.001). The most powerful predictor affecting cord adiponectin was the result of maternal 75 g oral glucose tolerance test. The cord IGF-I values between the GDM group and the non-GDM group were not different (P=0.834). Neonates with the heavier birth weight had the higher cord IGF-I levels. The most powerful predictor affecting cord IGF-I was birth weight and the next was maternal parity. Conclusion: Both cord blood adiponectin and IGF-I were associated with fetal growth, but IGF-I was a more general and direct factor affecting fetal body size, and adiponectin seemed to have more association with insulin sensitivity than growth.